{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/scgpt_new/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/root/anaconda3/envs/scgpt_new/lib/python3.10/site-packages/torch_npu/dynamo/__init__.py:18: UserWarning: Register eager implementation for the 'npu' backend of dynamo, as torch_npu was not compiled with torchair.\n",
      "  warnings.warn(\n",
      "/root/anaconda3/envs/scgpt_new/lib/python3.10/site-packages/torch_npu/contrib/transfer_to_npu.py:164: ImportWarning: \n",
      "    *************************************************************************************************************\n",
      "    The torch.Tensor.cuda and torch.nn.Module.cuda are replaced with torch.Tensor.npu and torch.nn.Module.npu now..\n",
      "    The torch.cuda.DoubleTensor is replaced with torch.npu.FloatTensor cause the double type is not supported now..\n",
      "    The backend in torch.distributed.init_process_group set to hccl now..\n",
      "    The torch.cuda.* and torch.cuda.amp.* are replaced with torch.npu.* and torch.npu.amp.* now..\n",
      "    The device parameters have been replaced with npu in the function below:\n",
      "    torch.logspace, torch.randint, torch.hann_window, torch.rand, torch.full_like, torch.ones_like, torch.rand_like, torch.randperm, torch.arange, torch.frombuffer, torch.normal, torch._empty_per_channel_affine_quantized, torch.empty_strided, torch.empty_like, torch.scalar_tensor, torch.tril_indices, torch.bartlett_window, torch.ones, torch.sparse_coo_tensor, torch.randn, torch.kaiser_window, torch.tensor, torch.triu_indices, torch.as_tensor, torch.zeros, torch.randint_like, torch.full, torch.eye, torch._sparse_csr_tensor_unsafe, torch.empty, torch._sparse_coo_tensor_unsafe, torch.blackman_window, torch.zeros_like, torch.range, torch.sparse_csr_tensor, torch.randn_like, torch.from_file, torch._cudnn_init_dropout_state, torch._empty_affine_quantized, torch.linspace, torch.hamming_window, torch.empty_quantized, torch._pin_memory, torch.Tensor.new_empty, torch.Tensor.new_empty_strided, torch.Tensor.new_full, torch.Tensor.new_ones, torch.Tensor.new_tensor, torch.Tensor.new_zeros, torch.Tensor.to, torch.nn.Module.to, torch.nn.Module.to_empty\n",
      "    *************************************************************************************************************\n",
      "    \n",
      "  warnings.warn(msg, ImportWarning)\n",
      "/root/riceFM/tutorials/../ricefm/model/model_main.py:30: UserWarning: flash_attn is not installed\n",
      "  warnings.warn(\"flash_attn is not installed\")\n"
     ]
    }
   ],
   "source": [
    "# %%\n",
    "import copy\n",
    "import gc\n",
    "import json\n",
    "import os\n",
    "from pathlib import Path\n",
    "import shutil\n",
    "import sys\n",
    "import time\n",
    "import traceback\n",
    "from typing import List, Tuple, Dict, Union, Optional\n",
    "import warnings\n",
    "import pandas as pd\n",
    "# from . import asyn\n",
    "import pickle\n",
    "import torch\n",
    "from anndata import AnnData\n",
    "import scanpy as sc\n",
    "# import scvi\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import wandb\n",
    "from scipy.sparse import issparse\n",
    "import matplotlib.pyplot as plt\n",
    "from torch import nn\n",
    "\n",
    "from torch_npu.contrib import transfer_to_npu\n",
    "import torch_npu\n",
    "\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import adjusted_rand_score, normalized_mutual_info_score\n",
    "from torchtext.vocab import Vocab\n",
    "from torchtext._torchtext import (\n",
    "    Vocab as VocabPybind,\n",
    ")\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "sys.path.insert(0, \"../\")\n",
    "import ricefm as scg\n",
    "from ricefm.model.model_main import TransformerModel, AdversarialDiscriminator\n",
    "from ricefm.tokenizer import tokenize_and_pad_batch, random_mask_value\n",
    "from ricefm.loss import (\n",
    "    masked_mse_loss,\n",
    "    masked_relative_error,\n",
    "    criterion_neg_log_bernoulli,\n",
    ")\n",
    "from ricefm.tokenizer.gene_tokenizer import GeneVocab\n",
    "from ricefm.preprocess import Preprocessor\n",
    "from ricefm import SubsetsBatchSampler\n",
    "from ricefm.utils import set_seed, category_str2int, eval_scib_metrics\n",
    "\n",
    "from torch.utils.data.sampler import RandomSampler, SequentialSampler\n",
    "\n",
    "\n",
    "# sc.set_figure_params(figsize=(6, 6))\n",
    "os.environ[\"KMP_WARNINGS\"] = \"off\"\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set hyperparameter_defaults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparameter_defaults = dict(\n",
    "    seed=0,\n",
    "\n",
    "    # model_file = '/root/riceFM/tutorials/save/dev_SRP250946-Nov06-21-47/model.pt',\n",
    "    model_file = '/root/riceFM_save/eval-Nov05-18-46-2025/best_model.pt',\n",
    "    # model_file = \"/share/appspace_data/shared_groups/BGI/USERS/lichuanxiu/scGPT-multiomic-B/run/save/eval-Oct21-20-30-2025/best_model.pt\",\n",
    "    model_param_file = '/root/riceFM/tutorials/save/dev_SRP250946-Nov06-21-47/args.json',\n",
    "    vocab_file = '/root/riceFM/tutorials/save/dev_SRP250946-Nov06-21-47/vocab.json',\n",
    "    input_file = '/root/celltype_annotation/SRP250946/SRX7814224.h5ad',\n",
    "    output_path = '/root/riceFM/tutorials/save/dev_SRP250946-Nov06-21-47/',\n",
    "    celltype_labels = 'Celltype',\n",
    "\n",
    "    mask_ratio=0.0,\n",
    "    epochs=10,\n",
    "    n_bins=51,\n",
    "    MVC=False, # Masked value prediction for cell embedding\n",
    "    ecs_thres=0.0, # Elastic cell similarity objective, 0.0 to 1.0, 0.0 to disable\n",
    "    dab_weight=0.0,\n",
    "    lr=1e-4,\n",
    "    batch_size=32,\n",
    "    layer_size=256,\n",
    "    nlayers=6,  # number of nn.TransformerEncoderLayer in nn.TransformerEncoder\n",
    "    nhead=4,  # number of heads in nn.MultiheadAttention\n",
    "    dropout=0.2,  # dropout probability\n",
    "    schedule_ratio=0.9,  # ratio of epochs for learning rate schedule\n",
    "    save_eval_interval=5,\n",
    "    pre_norm=False,\n",
    "    amp=True,  # Automatic Mixed Precision\n",
    "    include_zero_gene = False,\n",
    "    freeze = False, #freeze\n",
    "    DSBN = False,  # Domain-spec batchnorm\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'seed': 0, 'model_file': '/root/riceFM_save/eval-Nov05-18-46-2025/best_model.pt', 'model_param_file': '/root/riceFM/tutorials/save/dev_SRP250946-Nov06-21-47/args.json', 'vocab_file': '/root/riceFM/tutorials/save/dev_SRP250946-Nov06-21-47/vocab.json', 'input_file': '/root/celltype_annotation/SRP250946/SRX7814224.h5ad', 'output_path': '/root/riceFM/tutorials/save/dev_SRP250946-Nov06-21-47/', 'celltype_labels': 'Celltype', 'mask_ratio': 0.0, 'epochs': 10, 'n_bins': 51, 'MVC': False, 'ecs_thres': 0.0, 'dab_weight': 0.0, 'lr': 0.0001, 'batch_size': 32, 'layer_size': 256, 'nlayers': 6, 'nhead': 4, 'dropout': 0.2, 'schedule_ratio': 0.9, 'save_eval_interval': 5, 'fast_transformer': True, 'pre_norm': False, 'amp': True, 'include_zero_gene': False, 'freeze': False, 'DSBN': False}\n"
     ]
    }
   ],
   "source": [
    "run = wandb.init(\n",
    "    config=hyperparameter_defaults,\n",
    "    project=\"ricefm\",\n",
    "    reinit=True,\n",
    "    settings=wandb.Settings(start_method=\"fork\"),\n",
    "    mode=\"disabled\"\n",
    ")\n",
    "config = wandb.config\n",
    "print(config)\n",
    "\n",
    "set_seed(config.seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# settings for input and preprocessing\n",
    "pad_token = \"<pad>\"\n",
    "special_tokens = [pad_token, \"<cls>\", \"<eoc>\"]\n",
    "mask_ratio = config.mask_ratio\n",
    "mask_value = \"auto\"  # for masked values, now it should always be auto\n",
    "\n",
    "include_zero_gene = config.include_zero_gene  # if True, include zero genes among hvgs in the training\n",
    "max_seq_len = 3001\n",
    "n_bins = config.n_bins\n",
    "\n",
    "# input/output representation\n",
    "input_style = \"binned\"  # \"normed_raw\", \"log1p\", or \"binned\"\n",
    "output_style = \"binned\"  # \"normed_raw\", \"log1p\", or \"binned\"\n",
    "\n",
    "# settings for training\n",
    "MLM = False  # whether to use masked language modeling, currently it is always on.\n",
    "CLS = True  # celltype classification objective\n",
    "ADV = False  # Adversarial training for batch correction\n",
    "CCE = False  # Contrastive cell embedding objective\n",
    "MVC = config.MVC  # Masked value prediction for cell embedding\n",
    "ECS = config.ecs_thres > 0  # Elastic cell similarity objective\n",
    "DAB = False  # Domain adaptation by reverse backpropagation, set to 2 for separate optimizer\n",
    "INPUT_BATCH_LABELS = False  # TODO: have these help MLM and MVC, while not to classifier\n",
    "input_emb_style = \"continuous\"  # \"category\" or \"continuous\" or \"scaling\"\n",
    "cell_emb_style = \"cls\"  # \"avg-pool\" or \"w-pool\" or \"cls\"\n",
    "adv_E_delay_epochs = 0  # delay adversarial training on encoder for a few epochs\n",
    "adv_D_delay_epochs = 0\n",
    "mvc_decoder_style = \"inner product\"\n",
    "ecs_threshold = config.ecs_thres\n",
    "dab_weight = config.dab_weight\n",
    "\n",
    "explicit_zero_prob = MLM and include_zero_gene  # whether explicit bernoulli for zeros\n",
    "do_sample_in_train = False and explicit_zero_prob  # sample the bernoulli in training\n",
    "\n",
    "per_seq_batch_sample = False\n",
    "\n",
    "# settings for optimizer\n",
    "lr = config.lr  # TODO: test learning rate ratio between two tasks\n",
    "lr_ADV = 1e-3  # learning rate for discriminator, used when ADV is True\n",
    "batch_size = config.batch_size\n",
    "eval_batch_size = config.batch_size\n",
    "epochs = config.epochs\n",
    "schedule_interval = 1\n",
    "\n",
    "# settings for the model\n",
    "embsize = config.layer_size  # embedding dimension\n",
    "d_hid = config.layer_size  # dimension of the feedforward network in TransformerEncoder\n",
    "nlayers = config.nlayers  # number of TransformerEncoderLayer in TransformerEncoder\n",
    "nhead = config.nhead  # number of heads in nn.MultiheadAttention\n",
    "dropout = config.dropout  # dropout probability\n",
    "\n",
    "# logging\n",
    "log_interval = 100  # iterations\n",
    "save_eval_interval = config.save_eval_interval  # epochs\n",
    "do_eval_scib_metrics = True\n",
    "\n",
    "\n",
    "#sc--->0   st--->1\n",
    "mod_type = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% validate settings\n",
    "assert input_style in [\"normed_raw\", \"log1p\", \"binned\"]\n",
    "assert output_style in [\"normed_raw\", \"log1p\", \"binned\"]\n",
    "assert input_emb_style in [\"category\", \"continuous\", \"scaling\"]\n",
    "if input_style == \"binned\":\n",
    "    if input_emb_style == \"scaling\":\n",
    "        raise ValueError(\"input_emb_style `scaling` is not supported for binned input.\")\n",
    "elif input_style == \"log1p\" or input_style == \"normed_raw\":\n",
    "    if input_emb_style == \"category\":\n",
    "        raise ValueError(\n",
    "            \"input_emb_style `category` is not supported for log1p or normed_raw input.\"\n",
    "        )\n",
    "\n",
    "if input_emb_style == \"category\":\n",
    "    mask_value = n_bins + 1\n",
    "    pad_value = n_bins  # for padding gene expr values\n",
    "    n_input_bins = n_bins + 2\n",
    "else:\n",
    "    mask_value = -1\n",
    "    pad_value = -2\n",
    "    n_input_bins = n_bins\n",
    "\n",
    "if ADV and DAB:\n",
    "    raise ValueError(\"ADV and DAB cannot be both True.\")\n",
    "DAB_separate_optim = True if DAB > 1 else False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "adata = sc.read(config.input_file)\n",
    "\n",
    "adata.obs[\"celltype\"] = adata.obs[config.celltype_labels].astype(\"category\")\n",
    "adata.obs[\"batch_id\"]  = adata.obs[\"str_batch\"] = \"0\"\n",
    "adata.obs['mod_type'] = mod_type\n",
    "\n",
    "data_is_raw = True\n",
    "filter_gene_by_counts = False\n",
    "\n",
    "# make the batch category column\n",
    "batch_id_labels = adata.obs[\"str_batch\"].astype(\"category\").cat.codes.values\n",
    "adata.obs[\"batch_id\"] = batch_id_labels\n",
    "celltype_id_labels = adata.obs[\"celltype\"].astype(\"category\").cat.codes.values\n",
    "celltypes = adata.obs[\"celltype\"].unique()\n",
    "num_types = len(np.unique(celltype_id_labels))\n",
    "id2type = dict(enumerate(adata.obs[\"celltype\"].astype(\"category\").cat.categories))\n",
    "adata.obs[\"celltype_id\"] = celltype_id_labels\n",
    "adata.var[\"gene_name\"] = adata.var.index.tolist()\n",
    "\n",
    "adata.uns.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "if config.model_file is not None:\n",
    "\n",
    "    model_file = config.model_file\n",
    "    model_config_file = config.model_param_file\n",
    "    vocab_file = config.vocab_file\n",
    "\n",
    "    vocab = GeneVocab.from_file(vocab_file)\n",
    "\n",
    "    for s in special_tokens:\n",
    "        if s not in vocab:\n",
    "            vocab.append_token(s)\n",
    "\n",
    "    adata.var[\"id_in_vocab\"] = [\n",
    "        1 if gene in vocab else -1 for gene in adata.var[\"gene_name\"]\n",
    "    ]\n",
    "    gene_ids_in_vocab = np.array(adata.var[\"id_in_vocab\"])\n",
    "    # logger.info(\n",
    "    #     f\"match {np.sum(gene_ids_in_vocab >= 0)}/{len(gene_ids_in_vocab)} genes \"\n",
    "    #     f\"in vocabulary of size {len(vocab)}.\"\n",
    "    # )\n",
    "    adata = adata[:, adata.var[\"id_in_vocab\"] >= 0]\n",
    "\n",
    "    # model\n",
    "    with open(model_config_file, \"r\") as f:\n",
    "        model_configs = json.load(f)\n",
    "    # logger.info(\n",
    "    #     f\"Resume model from {model_file}, the model args will override the \"\n",
    "    #     f\"config {model_config_file}.\"\n",
    "    # )\n",
    "    embsize = model_configs[\"embsize\"]\n",
    "    nhead = model_configs[\"nheads\"]\n",
    "    d_hid = model_configs[\"d_hid\"]\n",
    "    nlayers = model_configs[\"nlayers\"]\n",
    "    n_layers_cls = model_configs[\"n_layers_cls\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "riceFM - INFO - Filtering cells by counts ...\n",
      "riceFM - INFO - Normalizing total counts ...\n",
      "riceFM - INFO - Log1p transforming ...\n",
      "riceFM - INFO - Binning data ...\n"
     ]
    }
   ],
   "source": [
    "# set up the preprocessor, use the args to config the workflow\n",
    "preprocessor = Preprocessor(\n",
    "    use_key=\"X\",  # the key in adata.layers to use as raw data\n",
    "    filter_gene_by_counts=filter_gene_by_counts,  # step 1\n",
    "    filter_cell_by_counts=False,  # step 2\n",
    "    normalize_total=1e4,  # 3. whether to normalize the raw data and to what sum\n",
    "    result_normed_key=\"X_normed\",  # the key in adata.layers to store the normalized data\n",
    "    log1p=data_is_raw,  # 4. whether to log1p the normalized data\n",
    "    result_log1p_key=\"X_log1p\",\n",
    "    subset_hvg=False,  # 5. whether to subset the raw data to highly variable genes\n",
    "    hvg_flavor=\"seurat_v3\" if data_is_raw else \"cell_ranger\",\n",
    "    binning=n_bins,  # 6. whether to bin the raw data and to what number of bins\n",
    "    result_binned_key=\"X_binned\",  # the key in adata.layers to store the binned data\n",
    ")\n",
    "\n",
    "preprocessor(adata, batch_key=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SeqDataset(Dataset):\n",
    "    def __init__(self, data: Dict[str, torch.Tensor]):\n",
    "        self.data = data\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.data[\"gene_ids\"].shape[0]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return {k: v[idx] for k, v in self.data.items()}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_layer_key = {  # the values of this map coorespond to the keys in preprocessing\n",
    "    \"normed_raw\": \"X_normed\",\n",
    "    \"log1p\": \"X_med\",\n",
    "    \"binned\": \"X_binned\",\n",
    "}[input_style]\n",
    "\n",
    "\n",
    "all_counts = (\n",
    "    adata.layers[input_layer_key].A\n",
    "    if issparse(adata.layers[input_layer_key])\n",
    "    else adata.layers[input_layer_key]\n",
    ")\n",
    "genes = adata.var[\"gene_name\"].tolist()\n",
    "\n",
    "\n",
    "vocab.set_default_index(vocab[\"<pad>\"])\n",
    "gene_ids = np.array(vocab(genes), dtype=int)\n",
    "\n",
    "celltypes_labels = adata.obs[\"celltype_id\"].tolist()  # make sure count from 0\n",
    "celltypes_labels = np.array(celltypes_labels)\n",
    "\n",
    "batch_ids = adata.obs[\"batch_id\"].tolist()\n",
    "batch_ids = np.array(batch_ids)\n",
    "\n",
    "mod_types = adata.obs['mod_type'].tolist()\n",
    "mod_types = np.array(mod_types)\n",
    "\n",
    "tokenized_adata = tokenize_and_pad_batch(\n",
    "    all_counts,\n",
    "    gene_ids,\n",
    "    max_len=max_seq_len,\n",
    "    vocab=vocab,\n",
    "    pad_token=pad_token,\n",
    "    pad_value=pad_value,\n",
    "    append_cls=True,  # append <cls> token at the beginning\n",
    "    include_zero_gene=include_zero_gene,\n",
    ")\n",
    "\n",
    "input_values_test = random_mask_value(\n",
    "    tokenized_adata[\"values\"],\n",
    "    mask_ratio=mask_ratio,\n",
    "    mask_value=mask_value,\n",
    "    pad_value=pad_value,\n",
    ")\n",
    "\n",
    "\n",
    "data_pt = {\n",
    "    \"gene_ids\": tokenized_adata[\"genes\"],\n",
    "    \"values\": input_values_test,\n",
    "    \"target_values\": tokenized_adata[\"values\"],\n",
    "    \"batch_labels\": torch.from_numpy(batch_ids).long(),\n",
    "    \"mod_labels\": torch.from_numpy(mod_types).long(),\n",
    "    \"celltype_labels\": torch.from_numpy(celltypes_labels).long(),\n",
    "}\n",
    "\n",
    "dataset=SeqDataset(data_pt)\n",
    "\n",
    "\n",
    "sampler = SequentialSampler(dataset)\n",
    "\n",
    "data_loader = DataLoader(\n",
    "    dataset=dataset,\n",
    "    batch_size=eval_batch_size,\n",
    "    shuffle=False,\n",
    "    drop_last=False,\n",
    "    num_workers=min(len(os.sched_getaffinity(0)), eval_batch_size // 2),\n",
    "    sampler=sampler,\n",
    "    # pin_memory=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransformerModel(\n",
       "  (encoder): GeneEncoder(\n",
       "    (embedding): Embedding(40158, 256, padding_idx=40155)\n",
       "    (enc_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (value_encoder): ContinuousValueEncoder(\n",
       "    (dropout): Dropout(p=0.2, inplace=False)\n",
       "    (linear1): Linear(in_features=1, out_features=256, bias=True)\n",
       "    (activation): ReLU()\n",
       "    (linear2): Linear(in_features=256, out_features=256, bias=True)\n",
       "    (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (mod_encoder): ModEncoder(\n",
       "    (embedding): Embedding(2, 256)\n",
       "    (enc_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (transformer_encoder): TransformerEncoder(\n",
       "    (layers): ModuleList(\n",
       "      (0-5): 6 x TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=256, out_features=256, bias=True)\n",
       "        (dropout): Dropout(p=0.2, inplace=False)\n",
       "        (linear2): Linear(in_features=256, out_features=256, bias=True)\n",
       "        (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.2, inplace=False)\n",
       "        (dropout2): Dropout(p=0.2, inplace=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (decoder): ExprDecoder(\n",
       "    (fc): Sequential(\n",
       "      (0): Linear(in_features=256, out_features=256, bias=True)\n",
       "      (1): LeakyReLU(negative_slope=0.01)\n",
       "      (2): Linear(in_features=256, out_features=256, bias=True)\n",
       "      (3): LeakyReLU(negative_slope=0.01)\n",
       "      (4): Linear(in_features=256, out_features=1, bias=True)\n",
       "    )\n",
       "  )\n",
       "  (cls_decoder): ClsDecoder(\n",
       "    (_decoder): ModuleList(\n",
       "      (0): Linear(in_features=256, out_features=256, bias=True)\n",
       "      (1): ReLU()\n",
       "      (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "      (3): Linear(in_features=256, out_features=256, bias=True)\n",
       "      (4): ReLU()\n",
       "      (5): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "    )\n",
       "    (out_layer): Linear(in_features=256, out_features=9, bias=True)\n",
       "  )\n",
       "  (sim): Similarity(\n",
       "    (cos): CosineSimilarity()\n",
       "  )\n",
       "  (creterion_cce): CrossEntropyLoss()\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"npu:0\" if torch.npu.is_available() else \"cpu\")\n",
    "torch.npu.set_device(device)\n",
    "\n",
    "ntokens = len(vocab)  # size of vocabulary\n",
    "model = TransformerModel(\n",
    "    ntokens,\n",
    "    embsize,\n",
    "    nhead,\n",
    "    d_hid,\n",
    "    nlayers,\n",
    "    nlayers_cls=3,\n",
    "    n_cls=num_types if CLS else 1,\n",
    "    vocab=vocab,\n",
    "    dropout=dropout,\n",
    "    pad_token=pad_token,\n",
    "    pad_value=pad_value,\n",
    "    do_mvc=MVC,\n",
    "    do_dab=DAB,\n",
    "    use_batch_labels=INPUT_BATCH_LABELS,\n",
    "    # num_batch_labels=num_batch_types,\n",
    "    domain_spec_batchnorm=config.DSBN,\n",
    "    input_emb_style=input_emb_style,\n",
    "    n_input_bins=n_input_bins,\n",
    "    cell_emb_style=cell_emb_style,\n",
    "    mvc_decoder_style=mvc_decoder_style,\n",
    "    ecs_threshold=ecs_threshold,\n",
    "    explicit_zero_prob=explicit_zero_prob,\n",
    "    pre_norm=config.pre_norm,\n",
    ")\n",
    "if config.model_file is not None:\n",
    "    try:\n",
    "        model.load_state_dict(torch.load(model_file))\n",
    "    except:\n",
    "        # only load params that are in the model and match the size\n",
    "        model_dict = model.state_dict()\n",
    "        pretrained_dict = torch.load(model_file)\n",
    "        pretrained_dict = {\n",
    "            k: v\n",
    "            for k, v in pretrained_dict.items()\n",
    "            if k in model_dict and v.shape == model_dict[k].shape\n",
    "        }\n",
    "\n",
    "        model_dict.update(pretrained_dict)\n",
    "        model.load_state_dict(model_dict)\n",
    "\n",
    "model.to(device)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cell embedding:   0%|          | 0/496 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W VariableFallbackKernel.cpp:51] Warning: CAUTION: The operator 'aten::_nested_tensor_from_mask_left_aligned' is not currently supported on the NPU backend and will fall back to run on the CPU. This may have performance implications. (function npu_cpu_fallback)\n",
      "Cell embedding: 100%|██████████| 496/496 [01:47<00:00,  4.63it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "cell_embeddings = np.zeros((adata.shape[0], embsize), dtype=np.float32)\n",
    "# celltypes = []\n",
    "# with torch.no_grad():\n",
    "count = 0\n",
    "for batch_data in tqdm(data_loader, desc='Cell embedding'):\n",
    "\n",
    "    input_gene_ids = batch_data[\"gene_ids\"].to(device)\n",
    "    input_values = batch_data[\"values\"].to(device)\n",
    "    target_values = batch_data[\"target_values\"].to(device)\n",
    "    batch_labels = batch_data[\"batch_labels\"].to(device)\n",
    "    mod_labels = batch_data[\"mod_labels\"].to(device)\n",
    "    celltype_labels = batch_data[\"celltype_labels\"].to(device)\n",
    "\n",
    "\n",
    "    src_key_padding_mask = input_gene_ids.eq(vocab[pad_token])\n",
    "    with torch.cuda.amp.autocast(enabled=config.amp):\n",
    "        output = model(\n",
    "            input_gene_ids,\n",
    "            input_values,\n",
    "            src_key_padding_mask=src_key_padding_mask,\n",
    "            mod_types=mod_labels,\n",
    "            batch_labels=None,\n",
    "        )\n",
    "\n",
    "\n",
    "    embeddings = output['cell_emb']  # get the <cls> position embedding\n",
    "    # embeddings = embeddings.cpu().numpy()\n",
    "    embeddings = embeddings.cpu().detach().numpy()\n",
    "    cell_embeddings[count: count + len(embeddings)] = embeddings\n",
    "    count += len(embeddings)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "embedding shape: (15870, 256)\n"
     ]
    }
   ],
   "source": [
    "print('embedding shape:', cell_embeddings.shape)\n",
    "scg_cell_emb = pd.DataFrame(cell_embeddings, index=adata.obs_names)\n",
    "\n",
    "adata.obsm['X_riceFM'] = scg_cell_emb.values\n",
    "# adata.layers.clear()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recompute neighbors on rep X_riceFM instead of None\n",
      "Cluster for cluster_0.2 with leiden\n",
      "Cluster for cluster_0.4 with leiden\n",
      "Cluster for cluster_0.6 with leiden\n",
      "Cluster for cluster_0.8 with leiden\n",
      "Cluster for cluster_1.0 with leiden\n",
      "Cluster for cluster_1.2 with leiden\n",
      "Cluster for cluster_1.4 with leiden\n",
      "Cluster for cluster_1.6 with leiden\n",
      "Cluster for cluster_1.8 with leiden\n",
      "Cluster for cluster_2.0 with leiden\n",
      "NMI...\n",
      "ARI...\n",
      "Silhouette score...\n",
      "PC regression...\n",
      "Graph connectivity...\n",
      "riceFM - INFO -                                   0\n",
      "NMI_cluster/label          0.323266\n",
      "ARI_cluster/label          0.076694\n",
      "ASW_label                  0.487452\n",
      "ASW_label/batch                 NaN\n",
      "PCR_batch                  0.655611\n",
      "cell_cycle_conservation         NaN\n",
      "isolated_label_F1               NaN\n",
      "isolated_label_silhouette       NaN\n",
      "graph_conn                 0.912826\n",
      "kBET                            NaN\n",
      "iLISI                           NaN\n",
      "cLISI                           NaN\n",
      "hvg_overlap                     NaN\n",
      "trajectory                      NaN\n",
      "riceFM - INFO - Biological Conservation Metrics: \n",
      "ASW (cell-type): 0.4875, graph cLISI: nan, isolated label silhouette: nan, \n",
      "Batch Effect Removal Metrics: \n",
      "PCR_batch: 0.6556, ASW (batch): nan, graph connectivity: 0.9128, graph iLISI: nan\n"
     ]
    }
   ],
   "source": [
    "results = eval_scib_metrics(adata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.neighbors(adata, use_rep='X_riceFM')\n",
    "sc.tl.umap(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NMI_cluster/label': 0.32326566324157135,\n",
       " 'ARI_cluster/label': 0.07669409113071418,\n",
       " 'ASW_label': 0.48745210841298103,\n",
       " 'PCR_batch': 0.6556107440785065,\n",
       " 'graph_conn': 0.912826024010656,\n",
       " 'avg_bio': 0.2958039542617555}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 15870 × 25872\n",
       "    obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'Orig.ident', 'Percent.mt', 'Seurat_clusters', 'Celltype', 'Genotype', 'celltype', 'batch_id', 'str_batch', 'mod_type', 'celltype_id', 'n_counts', 'cluster_0.2', 'cluster_0.4', 'cluster_0.6', 'cluster_0.8', 'cluster_1.0', 'cluster_1.2', 'cluster_1.4', 'cluster_1.6', 'cluster_1.8', 'cluster_2.0', 'cluster'\n",
       "    var: 'name', 'gene_name', 'id_in_vocab'\n",
       "    uns: 'log1p', 'neighbors', 'cluster_0.2', 'cluster_0.4', 'cluster_0.6', 'cluster_0.8', 'cluster_1.0', 'cluster_1.2', 'cluster_1.4', 'cluster_1.6', 'cluster_1.8', 'cluster_2.0', 'umap'\n",
       "    obsm: 'bin_edges', 'X_riceFM', 'X_umap'\n",
       "    layers: 'X_normed', 'X_log1p', 'X_binned'\n",
       "    obsp: 'distances', 'connectivities'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# custom_colors = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#FF00FF', '#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#FF00FF']\n",
    "plt.rcParams['figure.figsize'] = (5, 4)\n",
    "# fig = sc.pl.umap(adata, color='Celltype_prediction', frameon=False, palette=custom_colors, title='', return_fig=True)\n",
    "# fig.savefig('/share/appspace_data/shared_groups/BGI/USERS/lichuanxiu/celltype_annotation/SRP250946/SRX7814224-AnnoModel-predicted-UMAP.pdf', dpi=300, bbox_inches='tight')\n",
    "fig = sc.pl.umap(adata, color='cluster_0.2',\n",
    "                    # legend_loc='none',\n",
    "                    frameon=False,\n",
    "                    title='', return_fig=True)\n",
    "# fig.savefig(config.output_path + 'SRX7814225_train_raw-X_riceFM-UMAP-ANNO.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "scgpt_new",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
